Open AccessIntelligent high‐throughput intervention testing platform in Daphnia
Author(s) -
Cho Yongmin,
JonasCloss Rachael A.,
Yampolsky Lev Y.,
Kirschner Marc W.,
Peshkin Leonid
Publication year - 2022
Publication title -
aging cell
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.103
H-Index - 140
eISSN - 1474-9726
pISSN - 1474-9718
DOI - 10.1111/acel.13571
Subject(s) - biology , daphnia , daphnia magna , model organism , branchiopoda , computational biology , cladocera , machine learning , computer science , crustacean , ecology , genetics , medicine , toxicity , gene
We present a novel platform for testing the effects of interventions on the life‐ and healthspan of a short‐lived freshwater organism with complex behavior and physiology—the planktonic crustacean Daphnia magna . Within this platform, dozens of complex behavioral features of both routine motion and response to stimuli are continuously quantified over large synchronized cohorts via an automated phenotyping pipeline. We build predictive machine‐learning models calibrated using chronological age and extrapolate onto phenotypic age. We further apply the model to estimate the phenotypic age under pharmacological perturbation. Our platform provides a scalable framework for drug screening and characterization in both life‐long and instant assays as illustrated using a long‐term dose‐response profile of metformin and a short‐term assay of well‐studied substances such as caffeine and alcohol.